System and method for human motion detection and tracking

ABSTRACT

A system and method for human motion detection and tracking are disclosed. In one embodiment, a smart device having an optical sensing instrument monitors a stage. Memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures an image frame from the optical sensing instrument. The image frame is then converted into a designated image frame format, which is provided to a pose estimator. A two-dimensional dataset is received from the pose estimator. The system then converts, using inverse kinematics, the two-dimensional dataset into a three-dimensional dataset, which includes time-independent static joint positions, and then calculates, using the three-dimensional dataset, the position of each of the respective plurality of body parts in the image frame.

PRIORITY STATEMENT & CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/362,299, entitled “System and Method for Human Motion Detection andTracking”, filed on Jun. 29, 2021 in the names of Nathanael LloydGingrich et al., now U.S. Pat. No. 11,331,006, issued on May 17, 2022;which claims priority from U.S. Patent Application No. 63/155,653,entitled “System and Method for Human Motion Detection and Tracking” andfiled on Mar. 2, 2021, in the names of Nathanael Lloyd Gingrich et al.;which is hereby incorporated by reference, in entirety, for allpurposes. This application is also a continuation-in-part of U.S. patentapplication Ser. No. 17/260,477, entitled “System and Method for HumanMotion Detection and Tracking”, filed on Jan. 14, 2021, in the name ofLongbo Kong, now U.S. Pat. No. 11,103,748, issued on Aug. 31, 2021;which is a 371 national entry application of PCT/US20/21262 entitled“System and Method for Human Motion Detection and Tracking” and filed onMar. 5, 2020, in the name of Longbo Kong; which claims priority fromU.S. Patent Application No. 62/814,147, entitled “System and Method forHuman Motion Detection and Tracking” and filed on Mar. 5, 2019, in thename of Longbo Kong; all of which are hereby incorporated by reference,in entirety, for all purposes.

TECHNICAL FIELD OF THE INVENTION

The present disclosure relates, in general, to biomechanical evaluationsand assessments, which are commonly referred to as range of motionassessments, and more particularly, to automating a biomechanicalevaluation process, including a range of motion assessment, andproviding recommended exercises to improve physiological inefficienciesof a user.

BACKGROUND OF THE INVENTION

Human beings have regularly undergone physical examinations byprofessionals to assess and diagnose their health issues. Healthcarehistory has been predominantly reactive to an adverse disease, injury,condition or symptom. Increasingly, in modern times, with more access toinformation, a preventative approach to healthcare has been gaininggreater acceptance. Musculoskeletal health overwhelmingly represents thelargest health care cost. Generally speaking, a musculoskeletal systemof a person may include a system of muscles, tendons and ligaments,bones and joints, and associated tissues that move the body and helpmaintain the physical structure and form. Health of a person'smusculoskeletal system may be defined as the absence of disease orillness within all of the parts of this system. When pain arises in themuscles, bones, or other tissues, it may be a result of either a suddenincident (e.g., acute pain) or an ongoing condition (e.g., chronicpain). A healthy musculoskeletal system of a person is crucial to healthin other body systems, and for overall happiness and quality of life.Musculoskeletal analysis, or the ability to move within certain ranges(e.g., joint movement) freely and with no pain, is therefore receivinggreater attention. However, musculoskeletal analysis has historicallybeen a subjective science, open to interpretation of the healthcareprofessional or the person seeking care.

In 1995, after years of research, two movement specialists, Gray Cookand Lee Burton, attempted to improve communication and develop a tool toimprove objectivity and increase collaboration efforts in the evaluationof musculoskeletal health. Their system, the Functional Movement Screen(FMS), is a series of seven (7) different movement types, measured andgraded on a scale of 0-3. While their approach did find some success inbringing about a more unified approach to movement assessments, thesubjectivity, time restraint and reliance on a trained and accreditedprofessional to perform the evaluation limited its adoption.Accordingly, there is a need for improved systems and methods formeasuring and analyzing physiological deficiency of a person andproviding corrective recommended exercises while minimizing thesubjectivity during a musculoskeletal analysis.

SUMMARY OF THE INVENTION

It would be advantageous to achieve systems and methods that wouldimprove upon existing limitations in functionality with respect tomeasuring and analyzing physiological deficiency of a person. It wouldalso be desirable to enable a computer-based electronics and softwaresolution that would provide enhanced goniometry serving as a basis forfurnishing corrective recommended exercises while minimizing thesubjectivity during a musculoskeletal analysis. To better address one ormore of these concerns, a system and method for human motion detectionand tracking are disclosed. In one embodiment, a smart device having anoptical sensing instrument monitors a stage. A memory is accessible to aprocessor and communicatively coupled to the optical sensing instrument.The system captures an image frame from the optical sensing instrument.The image frame is then converted into a designated image frame format,which is provided to a pose estimator. A two-dimensional dataset isreceived from the pose estimator. The system then converts, usinginverse kinematics, the two-dimensional dataset into a three-dimensionaldataset, which includes time-independent static joint positions, andthen calculates, using the three-dimensional dataset, the position ofeach of the respective plurality of body parts in the image frame. Theseand other aspects of the invention will be apparent from and elucidatedwith reference to the embodiments described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the features and advantages of thepresent invention, reference is now made to the detailed description ofthe invention along with the accompanying figures in which correspondingnumerals in the different figures refer to corresponding parts and inwhich:

FIG. 1A is a schematic diagram depicting one embodiment of a system andmethod for human motion detection tracking for use, for example, with anintegrated goniometry system for measuring and analyzing physiologicaldeficiency of a person, such as a user, and providing correctiverecommended exercises according to an exemplary aspect of the teachingspresented herein;

FIG. 1B is a schematic diagram depicting one embodiment of the systemillustrated in FIG. 1A, wherein a user from a crowd has approached thesystem;

FIG. 2 is an illustration of a human skeleton;

FIG. 3 is an illustration of one embodiment of body parts identified bythe system;

FIG. 4 is a diagram depicting one embodiment of a set number ofrepetitions which are monitored and captured by the system;

FIG. 5 is a diagram depicting one embodiment of an image frameprocessing by the system;

FIG. 6 is a functional block diagram depicting one embodiment of a smartdevice, which forms a component of the system presented in FIGS. 1A and1B;

FIG. 7 is a conceptual module diagram depicting a software architectureof an integrated goniometry application of some embodiments;

FIG. 8 is a flow chart depicting one embodiment of a method forintegrated goniometric analysis according to exemplary aspects of theteachings presented herein;

FIG. 9 is a flow chart depicting one embodiment of a method for humanmotion detection and tracking according to the teachings presentedherein; and

FIG. 10 is a flow chart depicting another embodiment of a method forhuman motion detection and tracking.

DETAILED DESCRIPTION OF THE INVENTION

While the making and using of various embodiments of the presentinvention are discussed in detail below, it should be appreciated thatthe present invention provides many applicable inventive concepts, whichcan be embodied in a wide variety of specific contexts. The specificembodiments discussed herein are merely illustrative of specific ways tomake and use the invention, and do not delimit the scope of the presentinvention.

Referring initially to FIG. 1A, therein is depicted one embodiment of asystem for human motion detection and tracking that may be incorporatedinto an integrated goniometry system, for example, for performingautomated biomechanical movement assessments, which is schematicallyillustrated and designated 10. As shown, the integrated goniometrysystem 10 includes a smart device 12, which may function as anintegrated goniometer, having a housing 14 securing an optical sensinginstrument 16 and a display 18. The display 18 includes an interactiveportal 20 which provides prompts, such as an invitation prompt 22, whichmay greet a crowd of potential users U₁, U₂, and U₃ and invite a user toenter a stage 24, which may include markers 26 for foot placement of theuser standing at the markers 26 to utilize the integrated goniometrysystem 10. The stage 24 may be a virtual volumetric area 28, such as arectangular or cubic area, that is compatible with human exercisepositions and movement. The display 18 faces the stage 24 and theoptical sensing instrument 16 monitors the stage 24. A webcam may beincluded in some embodiments. It should be appreciated that the locationof the optical sensing instrument 16 and the webcam 17 may vary with thehousing 14. Moreover, the number of optical sensing instruments used mayvary also. Multiple optical sensing instruments or an array thereof maybe employed. It should be appreciated that the design and presentationof the smart device 12 may vary depending on application. By way ofexample, the smart device 12 and the housing 14 may be a device selectedfrom the group consisting of, with or without tripods, smart phones,smart watches, smart wearables, and tablet computers, for example.

Referring now to FIG. 1B, the user, user U₂, has entered the stage 24and the interactive portal 20 includes an exercise movement prompt 30providing instructions for the user U₂ on the stage 24 to execute a setnumber of repetitions of an exercise movement, such as a squat or abodyweight overhead squat, for example. In some implementations, theinteractions with the user U₂ are contactless and wearableless. A seriesof prompts on the interactive portal 20 instruct the user U₂ while theoptical sensing instrument 16 senses body point data of the user U₂during each exercise movement. Based on the sensed body point data, amobility score, an activation score, a posture score, a symmetry score,or any combination thereof, for example, may be calculated. A compositescore may also be calculated. One or more of the calculated scores mayprovide the basis for the integrated goniometry system 10 determining anexercise recommendation. As mentioned, a series of prompts on theinteractive portal instruct the user U₂ through repetitions of exercisemovements while the optical sensing instrument 16 senses body point dataof the user U₂. It should be appreciated that the smart device 12 may besupported by a server that provides various storage and supportfunctionality to the smart device 12. Further, the integrated goniometrysystem 10 may be deployed such that the server is remotely located in acloud C to service multiple sites with each site having a smart device.

Referring now to FIG. 2 and FIG. 3 , respective embodiments of a humanskeleton 60 and body parts identified by the integrated goniometrysystem 10 are depicted. Body part data 70 approximates certain locationsand movements of the human body, represented by the human skeleton 60.More specifically, the body part data 70 is captured by the opticalsensing instrument 16 and may include designated body part data 72 andsynthetic body part data 74. By way of example and not by way oflimitation, designated body part data 72 may include head data 82, neckdata 84, right shoulder data 86, left shoulder data 88, right upper armdata 90, left upper arm data 92, right elbow data 94, left elbow data96, right lower arm data 99, left lower arm data 101, right wrist data98, left wrist data 100, right hand data 102, left hand data 104, rightupper torso data 106, left upper torso data 108, right lower torso data110, left lower torso data 112, upper right leg data 114, upper left legdata 116, right knee data 118, left knee data 120, right lower leg data122, left lower leg data 124, right ankle data 126, left ankle data 128,right foot data 130, and left foot data 132. By way of example and notby way of limitation, synthetic body part data 74 may include right hip140, left hip 142, waist 144, top of spine 146, and middle of spine 148.As will be appreciated, the synthetic body part data 74 may include datacaptured by the optical sensing instrument 16 that includes locations inthe body in the rear of the person or data acquired through inference.

Referring now to FIG. 4 , image frames associated with a set number ofrepetitions of an exercise movement by the user U₂ are monitored andcaptured by the integrated goniometry system 10. As shown, in theillustrated embodiment, the user U₂ executes three squats andspecifically three bodyweight overhead squats at t₃, t₅, and t₇. Itshould be understood, however, that a different number of repetitionsmay be utilized and is within the teachings presented herein. That is, Niterations of movement is provided for by the teachings presentedherein. At times t₁ and t₉, the user U₂ is at a neutral position, whichmay be detected by sensing the body point data within the virtualvolumetric area 28 of the stage 24 or at t₉, an exercise end positionwhich is sensed with the torso in an upright position superposed abovethe left leg and the right leg with the left arm and right arm laterallyoffset to the torso.

At times t₂, t₄, t₆, and t₈, the user U₂ is at an exercise startposition. The exercise start position may be detected by the torso in anupright position superposed above the left leg and the right leg withthe left arm and the right arm superposed above the torso. From anexercise start position, the user U₂ begins a squat with an exercisetrigger. During the squat or other exercise movement, image frames arecollected. The exercise trigger may be displacement of the user from theexercise start position by sensing displacement of the body. Eachrepetition of the exercise movement, such as a squat, may be detected bysensing the body returning to its position corresponding to the exercisestart position. By way of example, the spine midpoint may be monitoredto determine or mark the completion of exercise movement repetitions.

Referring to FIG. 5 , by way of example, an image frame 150 is capturedhaving data at a time t₁. The image frame 150 includes at each imageelement, coordinate values that are monoptic and representtwo-dimensional coordinate values. Pre-processing occurs to the imageframe 150 to provide a designated image frame format 152, whichrepresents the pre-processing of the image frame 150. Suchpre-processing includes isolation of an object, i.e., the user U₂. Next,as shown at a pose estimator 154, probability distribution models 156generated by a neural network 158 are applied to the designated imageframe format 152 to identify body parts, such as skeleton points, asshown by two-dimensional dataset 160.

The two-dimensional dataset 160 is then converted via an application ofinverse kinematics 162 into a three-dimensional dataset 164 prior to theposition of each of the respective body parts being determined withinthe three-dimensional dataset 164. More particularly, as shown, thetwo-dimensional dataset 160 includes various of the designated body partdata 72, including the head data 82, the neck data 84, the rightshoulder data 86, the left shoulder data 88, the right elbow data 94,the left elbow data 96, the right wrist data 98, the left wrist data100, the right knee data 118, the left knee data 120, the right ankledata 126, the left ankle data 128, the right hip data 140, and the lefthip data 142, for example. A horizontal axis (designated y) and avertical axis (designated x) are defined in the two-dimensional dataset160. An intersection is fixed at F between the two-dimensional dataset160 and the horizontal axis (designated y) as a start of a kinematicchain 166. The intersection corresponds to feet of the user U₂.

Then, the smart device 12 calculates variable joint parameters underassumptions A₁, A₂, A₃, for example, that limb lengths L_(L1), L_(L2),L_(L3), L_(L4), L_(L5), L_(L6), L_(R1), L_(R2), L_(R3), L_(R4), L_(R5),L_(R6), have at least two hinge joints with a component of movement in adepth axis (designated z) perpendicular to the horizontal axis(designated y) and the vertical axis (designated x). In particular, theassumption A₁ relates to the knees, as represented by the right kneedata 118 and the left knee data 120, having a component of movement in adepth axis (designated z); the assumption A₂ relates to the hips, asrepresented by the right hip data 140 and the left hip data 142, havinga component of movement in a depth axis (designated z); and theassumption A₃ relates to the elbows, as represented by the right elbowdata 94 and the left elbow data 96, having a component of movement in adepth axis (designated z).

The limb length L_(L1) defines the length from the left ankle data 128to the left knee data 120; the limb length L_(L2) defines the lengthfrom the left knee data 120 to the left hip data 142; the limb lengthL_(L3) defines the length from the left hip data 142 to the leftshoulder data 88; the limb length L_(L4) defines the length from theleft shoulder data 88 to the neck data 84; the limb length L_(L5)defines the length from the left shoulder data 88 to the left elbow data96; and the limb length L_(L6) defines the length from the left elbowdata 96 to the left wrist data 100. Similarly, the limb lengths L_(R1),L_(R2), L_(R3), L_(R4), L_(R5), L_(R6) respectively relate to thesegments the right ankle data 126 to the right knee data 118, the rightknee data 118 to the right hip data 140, the right hip data 140 to theright shoulder data 86, the right shoulder data 86 to the neck data 84;the right shoulder data 86 to the right elbow data 94, and the rightelbow data 94 to the right wrist data 98. The limb length L_(C) relatesto the length from the neck data 84 to the head data 82.

The smart device 12 also calculates variable joint parameters withrespect to limb lengths of the user U₂ required to place the ends of thekinematic chain 166 in a given position and orientation relative to thestart of the kinematic chain 166 at the fixed intersection F. Theposition of each of the body parts in the image frame 150, which were intwo dimensions (e.g., x_(n), y_(n)) is calculated with respect to theimage frame 150 to provide three-dimensional coordinates (e.g. x_(n),y_(n), z_(n)) and provide joint positions, for example, such as anglealpha_(n).

Referring to FIG. 6 , within the housing 14 of the smart device 12, aprocessor 180, memory 182, and storage 184 are interconnected by abusing architecture 186 within a mounting architecture that alsointerconnects a network interface 188, a camera 190, including an imagecamera input 192 and/or image camera 194, inputs 196, outputs 198, andthe display 18. The processor 180 may process instructions for executionwithin the smart device 12 as a computing device, including instructionsstored in the memory 182 or in storage 184. The memory 182 storesinformation within the computing device. In one implementation, thememory 182 is a volatile memory unit or units. In anotherimplementation, the memory 182 is a non-volatile memory unit or units.The storage 184 provides capacity that is capable of providing massstorage for the smart device 12. The network interface 188 may provide apoint of interconnection, either wired or wireless, between the smartdevice 12 and a private or public network, such as the Internet. Thevarious inputs 196 and outputs 198 provide connections to and from thecomputing device, wherein the inputs 196 are the signals or datareceived by the smart device 12, and the outputs 198 are the signals ordata sent from the smart device 12. The display 18 may be an electronicdevice for the visual presentation of data and may, as shown in FIG. 6 ,be an input/output display providing touchscreen control. The camera 190may be enabled by an image camera input 192 that may provide an input tothe optical sensing instrument 16, which may be a camera, a point-cloudcamera, a laser-scanning camera, an infrared sensor, an RGB camera, or adepth camera, for example, or the camera 190 may be an image camera 194directly integrated into the smart device 12. By way of further example,the optical sensing instrument 16 may utilize technology such as time offlight, structured light, or stereo technology. By way of still furtherexample, in instances where the optical sensing instrument 16 is a depthcamera, an RGB camera, a color camera, a structured light camera, a timeof flight camera, a passive stereo camera, or a combination thereof maybe employed. Further, it should be appreciated that the optical sensinginstrument 16 may include two or more optical sensing instruments; thatis, more than one sensing instrument may be employed. As mentioned, thesmart device 12 and the housing 14 may be a device selected from thegroup consisting of (with or without tripods) smart phones, smartwatches, smart wearables, and tablet computers, for example.

The memory 182 and storage 184 are accessible to the processor 180 andinclude processor-executable instructions that, when executed, cause theprocessor 180 to execute a series of operations. In a first series ofoperations, the processor-executable instructions cause the processor180 to display an invitation prompt on the interactive portal. Theinvitation prompt provides an invitation to the user to enter the stageprior to the processor-executable instructions causing the processor 180to detect the user on the stage by sensing body point data within thevirtual volumetric area 28. By way of example and not by way oflimitation, the body point data may include first torso point data,second torso point data, first left arm point data, second left armpoint data, first right arm point data, second right arm point data,first left leg point data, second left leg point data, first right legpoint data, and second right leg point data, for example.

The processor-executable instructions cause the processor 180 to displaythe exercise movement prompt 30 on the interactive portal 20. Theexercise movement prompt 30 provides instructions for the user toexecute an exercise movement for a set number of repetitions with eachrepetition being complete when the user returns to an exercise startposition. The processor 180 is caused by the processor-executableinstructions to detect an exercise trigger. The exercise trigger may bedisplacement of the user from the exercise start position by sensingdisplacement of the related body point data. The processor-executableinstructions also cause the processor 180 to display an exercise endprompt on the interactive portal 20. The exercise end prompt providesinstructions for the user to stand in an exercise end position.Thereafter, the processor 180 is caused to detect the user standing inthe exercise end position.

The processor-executable instructions cause the processor 180 tocalculate one or more of several scores including calculating a mobilityscore by assessing angles using the body point data, calculating anactivation score by assessing position within the body point data,calculating a posture score by assessing vertical differentials withinthe body point data, and calculating a symmetry score by assessingimbalances within the body point data. The processor-executableinstructions may also cause the processor 180 to calculate a compositescore based on one or more of the mobility score, the activation score,the posture score, or the symmetry score. The processor-executableinstructions may also cause the processor 180 to determine an exerciserecommendation based on one or more of the composite score, the mobilityscore, the activation score, the posture score, or the symmetry score.

In a second series of operations, the processor-executable instructionscause the processor 180 to capture an image frame from the opticalsensing instrument 16. The image frame may include at each imageelement, two-dimensional coordinate values including a point related toa distance from the optical sensing instrument 16. Then the processor180 may be caused to convert the image frame into a designated imageframe format. The designated image frame format may include at eachimage element, coordinate values relative to the image frame. Theprocessor executable instructions may cause the processor 180 to access,through a pose estimator, and apply multiple probability distributionmodels to the designated image frame format to identify a respectiveplurality of body parts. In acquiring the multiple probabilitydistribution models, the probability distribution models may begenerated by a neural network. Next, the processor 180 may be caused tocalculate the position of each of the plurality of body parts in thedesignated image frame format and then calculate the position of each ofthe plurality of body parts in the image frame.

In a third series of operations, the processor-executable instructionscause the processor 180 to capture, via the optical sensing instrument,an image frame relative to a user in a line-of-sight with the opticalsensing instrument. The image frame may include at each image elementmonoptic coordinate values. The processor 180 is then caused by theprocessor-executable instructions to convert the image frame into adesignated image frame format prior to providing the designated imageframe format to a pose estimator. The processor 180 is caused to receivea two-dimensional dataset from the pose estimator and convert, usinginverse kinematics, the two-dimensional dataset into a three-dimensionaldataset. The processor 180 then calculates, using the three-dimensionaldataset, the position of each of the respective plurality of body partsin the image frame.

In a fourth series of operations, the processor-executable instructionscause the processor 180 to convert, using inverse kinematics, thetwo-dimensional dataset into a three-dimensional dataset by firstdefining a horizontal axis and a vertical axis in the two-dimensionaldataset. The processor-executable instructions then cause the processor180 to fix an intersection of the two-dimensional dataset and thehorizontal axis as a start of a kinematic chain. The intersection maycorrespond to feet of the user. The processor 180 is then caused tocalculate variable joint parameters under assumptions that the limblengths have at least two hinge joints with a component of movementperpendicular to the horizontal axis and the vertical axis. Then theprocessor 180 calculates the variable joint parameters with respect tolimb lengths of the user required to place ends of the kinematic chainin a given position and orientation relative to the start of thekinematic chain.

The processor-executable instructions presented hereinabove include, forexample, instructions and data which cause a general purpose computer,special purpose computer, or special purpose processing device toperform a certain function or group of functions. Processor-executableinstructions also include program modules that are executed by computersin stand-alone or network environments. Generally, program modulesinclude routines, programs, components, data structures, objects, andthe functions inherent in the design of special-purpose processors, orthe like, that perform particular tasks or implement particular abstractdata types. Processor-executable instructions, associated datastructures, and program modules represent examples of the program codemeans for executing steps of the systems and methods disclosed herein.The particular sequence of such executable instructions or associateddata structures represents examples of corresponding acts forimplementing the functions described in such steps and variations in thecombinations of processor-executable instructions and sequencing arewithin the teachings presented herein.

With respect to FIG. 6 , in some embodiments, the system for humanmotion detection and tracking may be at least partially embodied as aprogramming interface configured to communicate with a smart device.Further, in some embodiments, the processor 180 and the memory 182 ofthe smart device 12 and a processor and memory of a server may cooperateto execute processor-executable instructions in a distributed manner. Byway of example, in these embodiments, the server may be a local serverco-located with the smart device 12, or the server may be locatedremotely to the smart device 12, or the server may be a cloud-basedserver.

FIG. 7 conceptually illustrates a software architecture of someembodiments of an integrated goniometry application 250 that mayautomate the biomechanical evaluation process and provide recommendedexercises to improve physiological inefficiencies of a user. Such asoftware architecture may be embodied on an application installable on asmart device, for example. That is, in some embodiments, the integratedgoniometry application 250 is a stand-alone application or is integratedinto another application, while in other embodiments the applicationmight be implemented within an operating system 300. In someembodiments, the integrated goniometry application 250 is provided onthe smart device 12. Furthermore, in some other embodiments, theintegrated goniometry application 250 is provided as part of aserver-based solution or a cloud-based solution. In some suchembodiments, the integrated goniometry application 250 is provided via athin client. In particular, the integrated goniometry application 250runs on a server while a user interacts with the application via aseparate machine remote from the server. In other such embodiments,integrated goniometry application 250 is provided via a thick client.That is, the integrated goniometry application 250 is distributed fromthe server to the client machine and runs on the client machine.

The integrated goniometry application 250 includes a user interface (UI)interaction and generation module 252, management (user) interface tools254, data acquisition modules 256, image frame processing modules 258,image frame pre-processing modules 260, a pose estimator interface 261,mobility modules 262, inverse kinematics modules 263, stability modules264, posture modules 266, recommendation modules 268, and anauthentication application 270. The integrated goniometry application250 has access to activity logs 280, measurement and source repositories284, exercise libraries 286, and presentation instructions 290, whichpresents instructions for the operation of the integrated goniometryapplication 250 and particularly, for example, the aforementionedinteractive portal 20 on the display 18. In some embodiments, storages280, 284, 286, and 290 are all stored in one physical storage. In otherembodiments, the storages 280, 284, 286, and 290 are in separatephysical storages, or one of the storages is in one physical storagewhile the other is in a different physical storage.

The UI interaction and generation module 250 generates a user interfacethat allows, through the use of prompts, the user to quickly andefficiently perform a set of exercise movements to be monitored, withthe body point data collected from the monitoring furnishing anautomated biomechanical movement assessment scoring and relatedrecommended exercises to mitigate inefficiencies. Prior to thegeneration of automated biomechanical movement assessment scoring andrelated recommended exercises, the data acquisition modules 256 may beexecuted to obtain instances of the body point data via the opticalsensing instrument 16, which is then processed with the assistance ofthe image frame processing modules 258 and the image framepre-processing modules 260. The pose estimator interface 261 is utilizedto provide, in one embodiment, image frame pre-processing files createdby the image pre-processing modules 260 to a pose estimator to deriveskeleton points and other body point data. Following the collection ofthe body point data, the inverse kinematics modules 263 derivesthree-dimensional data including joint position data. Then, the mobilitymodules 262, stability modules 264, and the posture modules 266 areutilized to determine a mobility score, an activation score, and aposture score, for example. More specifically, in one embodiment, themobility modules 262 measure a user's ability to freely move a jointwithout resistance. The stability modules 264 provide an indication ofwhether a joint or muscle group may be stable or unstable. The posturemodules 266 may provide an indication of physiological stressespresented during a natural standing position. Following the assessmentsand calculations by the mobility modules 262, stability modules 264, andthe posture modules 266, the recommendation modules 268 may provide acomposite score based on the mobility score, the activation score, andthe posture score as well as exercise recommendations for the user. Theauthentication application 270 enables a user to maintain an account,including an activity log and data, with interactions therewith.

In the illustrated embodiment, FIG. 7 also includes the operating system300 that includes input device drivers 302 and a display module 304. Insome embodiments, as illustrated, the input device drivers 302 anddisplay module 304 are part of the operating system 300 even when theintegrated goniometry application 250 is an application separate fromthe operating system 300. The input device drivers 302 may includedrivers for translating signals from a keyboard, a touch screen, or anoptical sensing instrument, for example. A user interacts with one ormore of these input devices, which send signals to their correspondingdevice driver. The device driver then translates the signals into userinput data that is provided to the UI interaction and generation module252.

FIG. 8 depicts one embodiment of a method for integrated goniometricanalysis. At block 320, the methodology begins with the smart devicepositioned facing the stage. At block 322, multiple bodies aresimultaneously detected by the smart device in and around the stage. Asthe multiple bodies are detected, a prompt displayed on the interactiveportal of the smart device invites one of the individuals to the area ofthe stage in front of the smart device. At block 326, one of themultiple bodies is isolated by the smart device 12 and identified as anobject of interest once it separates from the group of multiple bodiesand enters the stage in front of the smart device 12. The identifiedbody, a user, is tracked as a body of interest by the smart device.

At block 328, the user is prompted to position himself into theappropriate start position which will enable the collection of abaseline measurement and key movement measurements during exercise. Atthis point in the methodology, the user is prompted by the smart deviceto perform the exercise start position and begin a set repetitions of anexercise movement. The smart device collects body point data to recordjoint angles and positions. At block 330, the smart device detects anexercise or movement trigger which is indicative of phase movementdiscrimination being performed in a manner that is independent of thebody height, width, size or shape of the user.

At block 332, the user is prompted by the smart device to repeat theexercise movement as repeated measurements provide more accurate andrepresentative measurements. A repetition is complete when the body ofthe user returns to the exercise start position. The user is provided aprompt to indicate when the user has completed sufficient repetitions ofthe exercise movement. With each repetition, once in motion, monitoringof body movement will be interpreted to determine a maximum, minimum,and moving average for the direction of movement, range of motion, depthof movement, speed of movement, rate of change of movement, and changein the direction of movement, for example. At block 334, the repetitionsof the exercise movement are complete. Continuing to decision block 335,if the session is complete, then methodology advances to block 336. Ifthe session is not complete, then the methodology returns to the block322. At block 336, once the required number of repetitions of theexercise movement are complete, the user is prompted to perform anexercise end position, which is a neutral pose. Ending at block 338,with the exercise movements complete, the integrated goniometrymethodology begins calculating results and providing the results and anyexercise recommendations to the user.

FIG. 9 and FIG. 10 show the methodology in more detail with elements 350through 366 and elements 380 through 410. Referring now to FIG. 9 , themethodology begins with block 350 and continues to block 352 where animage frame is captured. The image frame may be captured by the opticalsensing instrument. By way of example and not by way of limitation,image frame acquisition may involve obtaining raw image frame data fromthe camera. Additionally, in some embodiments, the image frame iscaptured of a user at a known location performing a known movement, suchas a squat. At block 354, pre-processing of the image frame occurs. Aspreviously discussed, during pre-processing, the image frame isconverted into a designated image frame format such that at each imageelement monoptic coordinate values are present relative to the imageframe. Also, during the pre-processing, the object—the body of theuser—may be isolated. At block 356, the image frame is converted intothe designated image frame format before the submission at block 358 toa pose estimator for the application of a probability distribution modelor models occurs for the body parts. Following the return of the datafrom the pose estimator, at block 360, inverse kinematics are applied toinfer three-dimensional data, such as joint positions, from thetwo-dimensional data.

The three-dimensional dataset may include time-independent static jointpositions. It is very common for applications using body pose estimationto be focused on time domain measurements, like attempting to gauge thespeed or direction of a movement by comparing joint or limb positionsacross multiple video frames. Often the goal is to classify the observedmovement, for instance using velocity or acceleration data todifferentiate falling down from sitting down. In contrast, in someembodiments, the systems and methods presented herein focus onaccumulating a dataset of static joint positions that can be used toaccurately calculate relevant angles between body parts to assess asession of multiple overhead squats. In these embodiments, the systemsand methods know in advance exactly where the user is located in theframe and what the movement will be. It is not necessary to use timedomain data to identify the movement being performed or to estimate thespeed at which it is performed.

In these embodiments, the assessment does not utilize any time domaindata to analyze and score performance of the overhead squats. The onlyreason for use of the time domain data (i.e., across multiple videoframes) may be to examine joint position changes in the time domain todetermine when a user is no longer moving to inform a prompt to providenext step guidance. While the created dataset contains joint positiondata for a group of sequential video frames, the analysis of that datais strictly time-independent. The analysis of the joint position framedata for a squat would be the same no matter if the frames were analyzedin the order they were captured or in random order, since angles orscores are not calculated in the time domain. That is, the systems andmethods presented herein calculate, using the three-dimensional datasetin a non-time domain manner, a position of each of a respectiveplurality of body parts in the image frame. The position of each of thebody parts may be calculated more accurately at block 364 before theposition of each body part is mapped at block 366 and the processconcludes at block 368.

Referring now to FIG. 10 , the methodology is initiated with theoperation of the camera at block 380 to ensure the camera is level. Atblock 382, the camera captures an image frame, and the methodologydetects a body at decision block 384. If a body is not detected, thenthe methodology returns to block 382. On the other hand, if a body isdetected, then the position of the body is evaluated at decision block386. If the position of the body has issues, such as the body not beingcompletely in the frame or the body not being square with respect to theframe, then the methodology proceeds to block 388, where a correction ispresented to assist the user with correcting the error beforere-evaluation at decision block 386.

Once the position at decision block 386 is approved, then themethodology advances to posture guidance at block 390 before the postureis evaluated at decision block 392. If the posture of the user iscorrect, then the methodology advances to decision block 394. On theother hand if the user's pose does not present the correct posture, thenthe methodology returns to block 390 where posture guidance is provided.At decision block 394, if the pose is held long enough then themethodology advances to block 396 where limb length data is saved. Ifthe pose is not held long enough, then the process returns to decisionblock 392.

At block 398, session guidance starts and the session, which presentsexercises or poses for the user to complete, continues until completionunless, as shown at decision block 400, the session is interrupted orotherwise not completed. If the session is not completed, as shown bydecision block 400, the methodology returns to decision block 384. Atblock 402 and block 404, the image frame is converted into the processedimage frame and recorded two-dimensional skeleton points and limblengths are utilized with inverse kinematics to calculate relevantangles between body parts. At block 406, the scoring algorithm isapplied before scores are presented at block 408. At decision block 410,the scores will be continued to be displayed until the user navigatesback to the main screen which returns the methodology to block 382.

The order of execution or performance of the methods and data flowsillustrated and described herein is not essential, unless otherwisespecified. That is, elements of the methods and data flows may beperformed in any order, unless otherwise specified, and that the methodsmay include more or less elements than those disclosed herein. Forexample, it is contemplated that executing or performing a particularelement before, contemporaneously with, or after another element are allpossible sequences of execution.

While this invention has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is, therefore, intended that the appended claimsencompass any such modifications or embodiments.

What is claimed is:
 1. A system for human motion detection and tracking,the system comprising: a smart device including a housing securing anoptical sensing instrument, a processor, a non-transitory memory, and anon-transitory storage therein, the smart device including a busingarchitecture communicatively interconnecting the optical sensinginstrument, the processor, the non-transitory memory, and thenon-transitory storage; and the non-transitory memory accessible to theprocessor, the non-transitory memory including processor-executableinstructions that, when executed by the processor, cause the system to:capture, via the optical sensing instrument, an image frame relative toa user in a line-of-sight with the optical sensing instrument, the imageframe including at each image element monoptic coordinate values,convert the image frame into a designated image frame format, derive atwo-dimensional dataset from the designated image frame format, convert,using inverse kinematics, the two-dimensional dataset into athree-dimensional dataset, the three-dimensional dataset including atime-independent plurality of static joint positions, and calculate,using the three-dimensional dataset in a non-time domain manner, aposition of each of a respective plurality of body parts in the imageframe.
 2. The system as recited in claim 1, wherein theprocessor-executable instructions to convert, using inverse kinematics,the two-dimensional dataset into the three-dimensional dataset furthercomprise processor-executable instructions that, when executed by theprocessor, cause the system to: define a horizontal axis and a verticalaxis in the two-dimensional dataset, fix an intersection of thetwo-dimensional dataset and the horizontal axis as a start of akinematic chain, and calculate variable joint parameters required toplace ends of the kinematic chain in a given position and orientationrelative to the start of the kinematic chain.
 3. The system as recitedin claim 1, wherein the processor-executable instructions to convert,using inverse kinematics, the two-dimensional dataset into thethree-dimensional dataset further comprise processor-executableinstructions that, when executed by the processor, cause the system to:define a horizontal axis and a vertical axis in the two-dimensionaldataset, fix an intersection of the two-dimensional dataset and thehorizontal axis as a start of a kinematic chain, the intersectioncorresponding to feet of the user, and calculate variable jointparameters with respect to limb lengths of the user required to placeends of the kinematic chain in a given position and orientation relativeto the start of the kinematic chain.
 4. The system as recited in claim1, wherein the processor-executable instructions to convert, usinginverse kinematics, the two-dimensional dataset into thethree-dimensional dataset further comprise processor-executableinstructions that, when executed by the processor, cause the system to:define a horizontal axis and a vertical axis in the two-dimensionaldataset, fix an intersection of the two-dimensional dataset and thehorizontal axis as a start of a kinematic chain, the intersectioncorresponding to feet of the user, calculate variable joint parametersunder assumptions that limb lengths of the user have at least two hingejoints with a component of movement perpendicular to the horizontal axisand the vertical axis, and calculate the variable joint parameters withrespect to the limb lengths of the user required to place ends of thekinematic chain in a given position and orientation relative to thestart of the kinematic chain.
 5. The system as recited in claim 4,wherein the at least two hinge joints comprise joints selected from thegroup consisting of knees, hips, and elbows.
 6. The system as recited inclaim 1, wherein the processor-executable instructions to derive thetwo-dimensional dataset further comprise processor-executableinstructions that, when executed by the processor, cause the system toaccess a neural network to derive the two-dimensional dataset.
 7. Thesystem as recited in claim 1, wherein the processor-executableinstructions to derive the two-dimensional dataset further compriseprocessor-executable instructions that, when executed by the processor,cause the system to utilize at least one probability distribution modelfor the designated image frame format to derive the two-dimensionaldataset.
 8. The system as recited in claim 1, wherein theprocessor-executable instructions to derive the two-dimensional datasetfurther comprise processor-executable instructions that, when executedby the processor, cause the system to determine skeleton point data andbody point data to derive the two-dimensional dataset.
 9. The system asrecited in claim 1, wherein the optical sensing instrument furthercomprises an instrument selected from the group consisting of a camera,a point-cloud camera, a laser-scanning camera, an infrared sensor, andan RGB camera.
 10. The system as recited in claim 1, wherein the opticalsensing instrument further comprises a technology selected from thegroup consisting of time of flight, structured light, and stereotechnology.
 11. The system as recited in claim 1, wherein the opticalsensing instrument further comprises a depth camera.
 12. The system asrecited in claim 11, wherein the depth camera further comprises a cameraselected from the group consisting of a structured light camera, a colorcamera, a time of flight camera, a passive stereo camera, andcombinations thereof.
 13. The system as recited in claim 1, wherein theoptical sensing instrument further comprises a plurality of cameras. 14.The system as recited in claim 1, wherein the housing of the smartdevice further comprises a display communicatively interconnected withthe busing architecture.
 15. The system as recited in claim 14, whereinthe memory further comprises processor-executable instructions that,when executed, cause the processor to display on an instruction prompton an interactive portal of the display, the instruction promptproviding instructions for the user.
 16. The system as recited in claim15, wherein the memory further comprises processor-executableinstructions that, when executed, cause the processor to: detect theuser in a baseline position, display an exercise prepare prompt on theinteractive portal, the exercise prepare prompt providing instructionsfor the user to stand in an exercise start position, detect the user inthe exercise start position, display an exercise movement prompt on theinteractive portal, the exercise movement prompt providing instructionsfor the user to execute an exercise movement for a set number ofrepetitions, each repetition being complete when the user returns to theexercise start position, and detect an exercise trigger, the exercisetrigger being displacement of the user from the exercise start positionby sensing displacement.
 17. The system as recited in claim 1, whereinthe smart device further comprises a device selected from the groupconsisting of smart phones, smart watches, smart wearables, and tabletcomputers.
 18. The system as recited in claim 1, wherein the monopticcoordinate values further comprise two-dimensional coordinate values.19. A system for human motion detection and tracking, the systemcomprising: a smart device including a housing securing an opticalsensing instrument, a processor, a non-transitory memory, and anon-transitory storage therein, the smart device including a busingarchitecture communicatively interconnecting the optical sensinginstrument, the processor, the non-transitory memory, and thenon-transitory storage; the non-transitory memory accessible to theprocessor, the non-transitory memory including firstprocessor-executable instructions that, when executed by the processor,cause the system to: capture, via the optical sensing instrument, animage frame relative to a user in a line-of-sight with the opticalsensing instrument, the image frame including at each image elementmonoptic coordinate values, convert the image frame into a designatedimage frame format, derive a two-dimensional dataset from the designatedimage frame format, convert, using inverse kinematics, thetwo-dimensional dataset into a three-dimensional dataset, thethree-dimensional dataset including a time-independent plurality ofstatic joint positions, and calculate, using the three-dimensionaldataset in a non-time domain manner, a position of each of therespective plurality of body parts in the image frame; and thenon-transitory memory accessible to the processor, the non-transitorymemory including second processor-executable instructions that, whenexecuted by the processor, cause the system to: define a horizontal axisand a vertical axis in the two-dimensional dataset, fix an intersectionof the two-dimensional dataset and the horizontal axis as a start of akinematic chain, and calculate variable joint parameters required toplace ends of the kinematic chain in a given position and orientationrelative to the start of the kinematic chain.
 20. A system for humanmotion detection and tracking, the system comprising: a smart deviceincluding a housing securing an optical sensing instrument, a processor,a non-transitory memory, and a non-transitory storage therein, the smartdevice including a busing architecture communicatively interconnectingthe optical sensing instrument, the processor, the non-transitorymemory, and the non-transitory storage; and the non-transitory memoryaccessible to the processor, the non-transitory memory including firstprocessor-executable instructions that, when executed by the processor,cause the system to: capture, via the optical sensing instrument, animage frame relative to a user in a line-of-sight with the opticalsensing instrument, the image frame including at each image elementmonoptic coordinate values, the user being at a known location, the userperforming a known movement, convert the image frame into a designatedimage frame format, derive a two-dimensional dataset from the designatedimage frame format, convert, using inverse kinematics, thetwo-dimensional dataset into a three-dimensional dataset, thethree-dimensional dataset including a time-independent plurality ofstatic joint positions, and calculate, using the three-dimensionaldataset in a non-time domain manner, a position of each of therespective plurality of body parts in the image frame.